Computer-controlled precision education and training

ABSTRACT

A system for streaming of contextual micro-content blocks for a workflow task to facilitate task performance by a user. The system includes a context sensing engine that processes one or more context inputs and generates an output based on the context inputs received from a front-end context monitoring appliance. The system includes a processing circuit having a navigation engine to navigate through digital information sources and search for information that matches one or more parameters of relevance for the workflow task. The processing circuit extracts computer-executable information files from the digital information sources that matches the one or more parameters of relevance for the workflow task and digitally processes the collected computer-executable information files into processed information blocks. The processing circuit includes a micro-content blocks creator for generating the contextual micro-content blocks from the processed information blocks. The micro-content blocks are delivered to the user when the micro-tasks are beginning.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/865,173 filed on Jul. 14, 2022, which is a continuation of U.S.patent application Ser. No. 16/712,213 filed on Dec. 12, 2019 and nowU.S. Pat. No. 11,423,500 issued on Aug. 23, 2022, the completedisclosures of which, in their entireties, are hereby incorporated byreference.

BACKGROUND Technical Field

The embodiments herein generally relate to computerized Internet-basededucation and training systems, and more particularly to acomputer-controlled remote-based learning system for facilitatingexecution of workflow tasks.

Description of the Related Art

Schools are a great way of learning and training. However, severalmodern technology requirements and environments for training systems aredependent on its context and reference. This means someone may not needto be taught everything in all possible ways. The context demands atleast some level of customization for the training and educationdelivery process in a digital environment.

Therefore, there is a need of a new intelligent and evolved digitalsystem for education and training that more efficiently allows executionof certain workflow tasks requiring training and education.

SUMMARY

In view of the foregoing, an embodiment herein provides a system forlive digital streaming of one or more contextual micro-content blocks inreal-time for a workflow task to facilitate task performance by a user.The system includes a context sensing engine that processes one or morecontext inputs associated with the user and converts them into processedinputs, and generates an output based on the one or more context inputsreceived from a front-end context monitoring appliance. The front-endcontext monitoring appliance is communicatively coupled to the contextsensing engine at a remote location and includes a context sensor thatdetects a context of the task performance and a Global PositioningService (GPS) device that detects geographical coordinates of a deviceassociated with the user. The system further includes a processingcircuit. The processing circuit includes a navigation engine thatnavigates through one or more digital information sources accessibleover a network and searches for information that matches one or moreparameters of relevance for the workflow task. The processing circuitfurther includes a computerized data collection wireless appliance thatextracts computer-executable information files from the one or moredigital information sources that matches the one or more parameters ofrelevance for the workflow task. The processing circuit further includesan information processing engine communicatively coupled to thecomputerized data collection wireless appliance. The information processengine digitally processes the collected computer-executable informationfiles into a plurality of processed information blocks. The processingcircuit further includes a micro-content blocks creator that generatesthe one or more contextual micro-content blocks from the plurality ofprocessed information blocks based on the output generated by thecontext sensing engine. The system further includes a micro-contentcommunication component that transmits the one or more contextualmicro-content blocks relating to the workflow task to the deviceassociated with the user at a time when the plurality of micro-tasks ofthe workflow task are about to start.

The system may further include an AI/ML (artificial intelligence/machinelearning) system remotely connected to the front-end context monitoringappliance and communicatively coupled to the context sensing engine. TheAI/ML system may receive a signal from the context sensing enginecontaining the processed context inputs and the output generated basedon the context inputs, process the context inputs to determine acontextual pattern for the workflow task utilizing a plurality ofintelligent and machine learning-based tools, and transmit a digitalsignal containing information pertinent to the contextual pattern to theprocessor.

The processing circuit may further include a filtering circuit thatfilters the plurality of processed information blocks based on thecontextual pattern associated with the user and the workflow task asindicative through the output generated by the context sensing enginecommunicatively coupled to the processing circuit and the AI/ML system.

The one or more micro-content blocks may include at least one of acontext-based micro-content block, a location-based micro-content block,a role-based micro-content block, and a skills-based micro-content blocksuch that the one or more contextual micro-content blocks matches to aplurality of micro-tasks of the workflow task.

The one or more contextual micro-content blocks may be time-stampedbefore transmission by the micro-content communication component to thedevice associated with the user for real-time delivery according to anoccurrence of the plurality of micro-tasks of the workflow task.

The one or more parameters of relevance may be defined based on one ormore of a plurality of digital inputs stored in a memory device, theplurality of digital inputs including a digital input indicative ofnature of the workflow task, a digital input indicative of specificmicro-tasks associated with the workflow task, a digital inputindicative of location of execution or performance of the workflow task,a digital input indicative of actor performing the task, a digital inputindicative of context of the workflow task, a digital input indicativeof skill-sets of the actor performing the workflow task, a digital inputindicative of role of the actor performing the workflow task.

The GPS device may retrieve real-time tracking location coordinatesassociated with an event occurrence for the plurality of micro-tasks ofthe workflow task. The GPS device may include a radio-navigation system.

The front-end context monitoring appliance may include an agent devicecoupled communicatively and operatively with the context sensing engine.

The agent device may be operated by deploying an installable agent,configured as a browser plugin, at the device associated with the user.

The system may further include a blockchain device that interacts withthe context sensing engine through a plurality of blockchain configureddistributed access points over a blockchain network.

The blockchain device may include a distributed trusted ledgers systemcontaining a plurality of distributed blockchain ledgers associated witha plurality of computing terminals such that each ledger stores a copyof a computer-executable file containing the context inputs and the oneor more contextual micro-content blocks associated with the workflowtask.

The blockchain device may include a blockchain database that stores thecollected computer-executable information files from the one or moredigital information sources and the one or more contextual micro-contentblocks.

Another embodiment herein provides a blockchain-enabled informationmanagement server for live digital streaming of one or more contextualmicro-content blocks in real-time for a workflow task to facilitate taskperformance by a user. The information management server includes acontext sensing engine that may process one or more context inputsassociated with the user and convert them into processed inputs, andgenerate an output based on the one or more context inputs received froma front-end context monitoring appliance. The front-end contextmonitoring appliance may be communicatively coupled to the contextsensing engine at a remote location. The front-end context monitoringappliance may include a context sensor that detects a context of thetask performance and a GPS device that detects geographical coordinatesof a device associated with the user. The system further includes aprocessing circuit. The processing circuit includes a navigation enginethat navigates through one or more digital information sourcesaccessible over a network and searches for information that matches oneor more parameters of relevance for the workflow task. The processingcircuit further includes a computerized data collection wirelessappliance that extracts computer-executable information files from theone or more digital information sources that matches the one or moreparameters of relevance for the workflow task. The processing circuitincludes an information processing engine communicatively coupled to thecomputerized data collection wireless appliance. The informationprocessing engine digitally processes the collected computer-executableinformation files into a plurality of processed information blocks. Theprocessing circuit includes a micro-content blocks creator forgenerating the one or more contextual micro-content blocks from theplurality of processed information blocks based on the output generatedby the context sensing engine.

The system further includes a micro-content communication component thattransmits the one or more contextual micro-content blocks relating tothe workflow task to the device associated with the user at a time whenthe plurality of micro-tasks of the workflow task are about to start.The system further includes a blockchain device that interacts with thecontext sensing engine through a plurality of blockchain configureddistributed access points over a blockchain network.

The blockchain device may include a distributed trusted ledgers systemcontaining a plurality of distributed blockchain ledgers associated witha plurality of computing terminals such that each ledger stores a copyof a computer-executable file containing the context inputs and the oneor more contextual micro-content blocks associated with the workflowtask.

These and other aspects of the embodiments herein will be betterappreciated and understood when considered in conjunction with thefollowing description and the accompanying drawings. It should beunderstood, however, that the following descriptions, while indicatingexemplary embodiments and numerous specific details thereof, are givenby way of illustration and not of limitation. Many changes andmodifications may be made within the scope of the embodiments hereinwithout departing from the spirit thereof, and the embodiments hereininclude all such modifications.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein will be better understood from the followingdetailed description with reference to the drawings, in which:

FIG. 1 illustrates an example of a computer architecture in whichvarious embodiments herein may operate;

FIG. 2 illustrates a schematic diagram of an information managementserver in accordance with an embodiment herein;

FIG. 3 illustrates a front-end context monitoring appliance connectedwith a context sensing engine in accordance with an embodiment herein;

FIG. 4 illustrates a blockchain-configured ecosystem architecturecontaining one or more components of the system of FIG. 1 in accordancewith an embodiment herein; and

FIG. 5 is a block diagram illustrating a computer system used inaccordance with the embodiments herein.

DETAILED DESCRIPTION

The embodiments herein and the various features and advantageous detailsthereof are explained more fully with reference to the non-limitingembodiments that are illustrated in the accompanying drawings anddetailed in the following description. Descriptions of well-knowncomponents are omitted so as to not unnecessarily obscure theembodiments herein. The examples used herein are intended merely tofacilitate an understanding of ways in which the embodiments herein maybe practiced and to further enable those of skill in the art to practicethe embodiments herein. Accordingly, the examples should not beconstrued as limiting the scope of the embodiments herein.

Referring now to the drawings, and more particularly to FIGS. 1 through5 , where similar reference characters denote corresponding featuresconsistently throughout the figures, there are shown preferredembodiments. In the drawings, the size and relative sizes of components,layers, and regions, etc. may be exaggerated for clarity.

FIG. 1 illustrates an example of a computer architecture in whichvarious embodiments herein may operate. The computer architecture mayinclude a system 102 for live digital streaming of one or morecontextual micro-content blocks in real-time for a workflow task 114 tofacilitate task performance by a user. The system 102 may include aninformation management server 104 configured for navigating through aplurality of information sources 106 a, 106 b, and 106 c (collectivelyreferred to as information sources 106, for the purpose of description)distributed remotely from one another and storing digital filesexecutable by a computer (such as the information management server 104)in a plurality of respective storage devices such as a storage device108 a, a storage device 108 b, and a storage device 108 c collectivelyreferred to as storage devices 108, for the purpose of description. Aninformation source of the information sources 106 may store itsrespective digital files in one or more storage devices 108 that areassociated with the respective information source. The informationsources 106 and the storage devices 108 shown in FIG. 1 are forillustration purposes only, otherwise the number of information sources106 and the number of storage systems 108 may be different. In someembodiments, there may be large number of information sources 106 andstorage systems 108 and a different number of information sources 106compared with the storage systems 108.

The information management server 104 may be connected to user devicesassociated with a plurality of users. As an example, for the purpose ofdescribing an embodiment, the information management server 104 is shownto be communicatively connected with one user device 112 through acommunication network 110, such as the Internet or an Intranet. The usermay perform a workflow task at a particular time and location such thatthe performance of the workflow task (alternatively referred to as a“task” without limitations herein) 114 occurs at a location remote fromthe location of the information management server 104. The user device112 is associated with the user and is also located remote from theinformation management server 104.

The workflow task 114 may involve a series of tasks called micro-taskssuch as a first task, a second task, a third task, and a fourth task,etc. without limitations in number. Each of these micro-tasks may beperformed in a particular sequence at particular locations andparticular time slots. These micro-tasks may be independent of oneanother, or totally or partially dependent on at least some of the othermicro-tasks.

The information management server 104 may be configured to monitor andreceive details pertinent to the workflow task 114 based on certaininputs received from the user device 112 and/or based on certainautomated transfer of digital messages from the user device 112 and itsassociated sensors and monitoring agents such as a front-end contextmonitoring appliance 310 as shown in FIG. 3 and described later. Theinformation management server 104 may serve the user device 112 one ormore micro-content blocks 116 based on the inputs received and/or thedigital messages, which may be indicative of the workflow task 114 alongwith associated respective micro-tasks and the context of the userdevice 112, user, and the performance of the workflow task 114 (referredto as contextual patterns).

The micro-content blocks 116 may be of a variety of types based on thetypes of contextual patterns associated with the user device 112 and theworkflow task 114. For example, the micro-content blocks 116 may includeat least one or more task context-based micro-content blocks 118, one ormore role-based micro-content blocks 120, one or more skill-basedmicro-content blocks 122, and one or more location-based micro-contentblocks 124. In an example, the various micro-content blocks 116 mayinclude information elements generated by mapping onto each micro-taskfor relevancy, the role of the user interacting for executing a specificmicro-task for relevancy, and the level of experience or skill sets ofthe user for a particular micro-task that is contained within theworkflow task 114 such that the one or more contextual micro-contentblocks 116 match to the plurality of micro-tasks of the workflow task.Therefore, the various micro-content blocks 116 provides a set ofstructured digital information blocks that are relevant and related anddeeply maps onto various micro-tasks when looked in association with theskillets of the user, location of the user, nature of micro-tasks andthe overall workflow task 114, role of the user in various micro-tasks,and the like. The micro content blocks 116 are further discussed below.

The illustrated information sources 106 may include online web sourcesand databases connected over the Internet, an electronic medical record(EHR), a medical information exchange (HIE), an image archivingcommunication system storing images, other localized but accessible datastores and the like without limitations. The information sources 106provide various types of digital information including medicalinformation to the information management server 104. For example, EHRsand web pages can each provide information such as medical information,diagnostic information, radiographs, and the like.

FIG. 2 , with reference to FIG. 1 , illustrates a schematic diagram ofthe information management server 104 in an embodiment. The informationmanagement server 104 may include a processing circuit also referred toas a processor 202 interchangeably and a context sensing engine 204. Theprocessing circuit 202 may include a navigation engine 206, acomputerized data collection wireless appliance 208, an informationprocessing engine 210, and a micro-content blocks generator 212. Theprocessing circuit 202 is configured to perform a variety of specializedprocessing tasks including navigating for relevant information,extracting the relevant information from the plurality of distributedinformation sources 106, and creating information elements calledmicro-information blocks as will be discussed later for delivery to theuser device 112 in accordance with the execution or performance of thespecific micro tasks. The processing circuit 202 may be capable ofexecuting pre-programmed instructions for performing specialized tasksassociated with operation of the information management server 104. Thevarious components of the processing circuit 202 are discussed hereafterin the document.

The processor 202 receives a portion of the digital information that theinformation management server 104 identifies as relevant and performsspecific tasks for processing such as but not limited to indexing,semantic meta tagging, matching, relevancy checking, curating,summarizing, indexing, organizing, processing, standardizing,transforming into micro-content blocks etc.

The navigation engine 206 is configured to navigate through one or moreof the digital information sources 106 accessible over the network 110and search for the digital information that matches one or moreparameters of relevance for the workflow task 114. The navigation engine206 may crawl through millions of web pages and other localizedrepositories for searching relevant information. The one or moreparameters of relevance may be defined based on the nature and contextof the workflow task 114, specific micro-tasks associated with theworkflow task 114, location of execution or performance of the workflowtask 114, actor performing the workflow task 114, context of theworkflow task 114, skill-sets of the person performing the workflow task114, role of the person performing the workflow task 114, and the like,each indicated through a respective digital input. These parameters maytogether be indicative of a contextual pattern for the workflow task114. An AI/ML (artificial intelligence/machine learning) system 214 mayutilize the various parameters of relevance associated with the workflowtask 114 and create a unified score for determining the contextualpattern associated with the workflow task 114. The AI/ML system 214 iscommunicatively coupled to the context sensing engine 204 and theprocessor 202.

The contextual pattern may be determined based on one or more contextinputs received and processed by the context sensing engine 204 asmonitored by the front-end context monitoring appliance 310 (shown inFIG. 3 ). The context sensing engine 204 may be configured to processthe one or more context inputs associated with the workflow task 114(including associated user and systems) and generate an output signalbased on the one or more context inputs. The context sensing engine 204may include or be coupled to a receiving circuit 234 to receive the oneor more context inputs including any manually provided user inputsindicative of the user behaviour etc. The context sensing engine 204 mayutilize the context inputs obtained from signals received from thefront-end context monitoring appliance 310 (of FIG. 3 ) indicative ofthe context in which the workflow task 114 is being performed.

As illustrated in FIG. 3 , with reference to FIGS. 1 and 2 , thefront-end context monitoring appliance 310 may include a context sensor302 to detect a context of the task performance, and a GPS device 304 todetect geographical coordinates of a user device 306 associated with theuser.

The Global Positioning Service (GPS) device 304 of the front-end contextmonitoring appliance 310 may help in real-time tracking of locationcoordinates associated with various event occurrences or performance ofthe workflow task 114 or respective micro-tasks by collecting theirlocation details. Real-time tracking may offer different challenges inthe tracking of the event occurrences or tasks depending on thecomplexity of the event occurrences when it is performed manually andtakes time, resources and manpower. Location data may be collected inmost cases by the GPS device 304 using, for example, a radio-navigationsystem; though in some other specific cases different locationtechnologies can be used. For example, in an ambulance service of ahospital, the GPS device 304 may help in real-time tracking of a patientbeing transferred, journey reports, stop reports, alerts, and scheduledreports for future and associated tasks performed by differentparticipants.

The front-end context monitoring appliance 310 may include an agentdevice 312 that may be coupled communicatively and operatively with theinformation management server 104. Similar agent devices may be coupledwith or deployed at other devices too that may be connected with theinformation management server 104. The agent device 312 may be operatedby deploying an installable agent 314 at the user device 306. In anexample, the installable agent 314 may be defined in the form of abrowser plugin installed by a user on the user device 306.

The processor 202 and the context sensing engine 204 are communicativelyconnected with the AI/ML system 214 in an embodiment. In an embodiment,the AI/ML system 214 may be an integral part of the processor 202 or thecontext sensing engine 204. In accordance with an embodiment, the AI/MLsystem 214 may receive a signal from the context sensing engine 204containing the processed context inputs. The AI/ML system 214 mayprocess the context inputs to determine a contextual pattern for theworkflow task 114 utilizing a plurality of intelligent and machinelearning-based tools. The AI/ML system 214 may include an automaticcontrol system 218, an artificial intelligence machine 220, and machinelearning tools 222. These components are discussed below.

In an example, the AI/ML system 214 may allow decision-making anddetermining the contextual pattern of the workflow task 114 based onreal-time evidence as generated by the context sensing engine 204utilizing and processing the context inputs received from the front-endcontext monitoring appliance 310. The real-time evidence may begenerated based on the context inputs monitored by the context sensor302, GPS device 304, and other devices. The context inputs allow theAI/ML system 214 to perform complex decision-making tasks to determinethe contextual pattern associated with the workflow task 114. The AI/MLsystem 214 may perform simple and tactical tasks smartly in the absenceof humans with the use of the artificial intelligence machine 220 andthe machine learning tools 222 because the computer-executable contextinputs are trustworthy and are obtained through direct environmentsassociated with the user, user device 306, and the workflow taskscenario. The AI/ML system 214 may generate the contextual pattern usingcertain predefined computer-executable rules that may be defined eitherby human manually or generated by the network 110 and/or based onlearning by the AI/ML system 214 based on past transactions andoperations over time. The contextual pattern may be indicative of theactual situation in which the workflow task 114 and its associated usermay behave while performing the workflow task 114 or the challenges thatmay come across in task execution or the learning and training that theuser may require for performing the workflow task 114 or respectivemicro-tasks effectively. Accordingly, appropriate steps may be taken bythe processor 202 for ensuring necessary support is provided to the useror the user device 306 at the right time.

In an example, the AI/ML system 214 may perform an automated analysis ofthe context inputs to determine the contextual pattern. The AI/ML system214 may generate AWL-based predictions of future expected behavior andlearning requirements by the user using the artificial intelligencemachine 220 and the machine learning tools 222 to timely guide atappropriate time in advance when certain information is required by theuser. The artificial intelligence machine 220 is configured to generatean output (such as the contextual pattern) smartly using a set of inputssuch as the context inputs so that the generated output addressrequirements of the user for executing a task more precisely andaccurately in a real-time scenario. In the process, the artificialintelligence machine 220 utilizes the machine learning tools 222. Themachine learning tools 222 are configured to train the artificialintelligence machine 220 how to learn over time with more repeatrequests for the micro content blocks by different users in differentcontexts performing different types of tasks. The machine learning tools222 are given access to data and allowed to learn on its own based onhistorical records and processing etc.

In an example, the AI/ML system 214 may carry out a predeterminedinference on the basis of the aggregated context inputs, and take actionin accordance with certain inference results generated as a result ofthe analysis by the AI/ML system 214. The automatic control system 218may be provided and adapted for a target action to be taken by theartificial intelligence machine 220 of the AI/ML system 214 on the basisof the aggregated context inputs and the inference results and generatea control output for taking a target action such as generating themicro-content blocks 116 as will be discussed later. In an example, theAI/ML system 214 may be adapted to drive the artificial intelligencemachine 220 on the basis of the inference results and the control outputfor past events stored in a memory circuit 224.

The AI/ML system 214 may generate the contextual pattern and transmit adigital signal 203 containing information pertinent to the contextualpattern to the processor 202. The processor 202 may perform a set ofprocessing tasks as described herein.

As discussed above, the pages and other data repositories are crawled bythe navigation engine 206 to identify the relevant information in viewof the workflow task 114 based on the one or more parameters ofrelevance. The processor 202 includes the computerized data collectionwireless appliance 208 that is configured to extract computer-executableinformation files from the one or more digital information sources 106that matches the one or more parameters of relevance for the workflowtask 114 as identified and crawled by the navigation engine 206.

The computerized data collection wireless appliance 208 may performcertain data collection tasks digitally to extract computer-executableinformation files from the one or more digital information sources 106that matches the one or more parameters of relevance for the workflowtask 114. In the example described herein, the computerized datacollection wireless appliance 208 may be permitted to generate and/orcollect data from the information sources 106 that are connected to thenetwork 110 and permitted to be accessed by the processor 202 orassociated entity such as an organization. The network 110 may be awireless or a physical network configured to operate as a peer networkin some embodiments or a global internetwork.

The data collection wireless appliance 208 or the processor 202 may becoupled communicatively with a local data reservoir 226 such that datacollection wireless appliance 208 may be configured to collect, store,and digitally manage data in the local data reservoir 226 that isextracted or collected from the information sources 106 in context ofthe workflow task 114 that may be a computer-executable task in someembodiments.

The information processing engine 210 that is communicatively coupled tothe computerized data collection wireless appliance 208 is configured todigitally process the collected computer-executable information filesinto a plurality of processed information blocks. The process oftransformation of the collected computer-executable information filesinto the processed information blocks may involve a series of steps suchas including, without limitations, meta tagging of the informationfiles, summarizing the information files, curation, high level relevancyanalysis for the workflow task 114, through an automated process with orwithout utilizing one or more operations by the AI/ML system 214, andthe like.

The micro-content blocks creator 212 of the processor 202 is configuredto generate the one or more contextual micro-content blocks 116 from theplurality of processed information blocks based on the output generatedby the context sensing engine 204 and/or the AI/ML system 214 indicativeof the one or more contextual patterns derived from the context inputsreceived in real-time or in advance from the workflow task 114scenarios, associated user, and the associated user device 306. Theprocessing circuit 202 may further include a filtering circuit 232configured to filter the plurality of processed information blocks basedon the one or more contextual patterns and the processed content inputsassociated with the user as indicative through the output generated bythe context sensing engine 204 and/or the AI/ML system 214communicatively coupled to the processing circuit 202. This may allowmore accurate and relevant information to be used for generating themicro-content blocks 116.

The filtering circuit 232 removes redundant or unwanted information froman information stream (such as the processed information blocks) usingautomated or computerized methods prior to presentation to the user inthe form of the micro content blocks. The filtering circuit 232 managesthe information overload and increment of the semantic signal-to-noiseratio. The filtering circuit 232 compares the processed informationblocks with certain reference characteristics such as the contextualpattern to determine what is noise versus what is important for aparticular workflow task or a series of micro tasks.

The one or more micro-content blocks 116 may include context-basedmicro-content blocks that are generated in view of the nature of theworkflow task 114 and the situation in which the workflow task 114 isbeing executed and the various micro-tasks associated with the workflowtask 114 and the nature of each micro-task thereof. The one or moremicro-content blocks 116 may include the location-based micro-contentblock 124 that is generated in view of the location of actual executionof the workflow task 114. The one or more micro-content blocks 116 mayinclude the role-based micro-content block 120 that is generated in viewof the role of the user in performance of the workflow task 114. The oneor more micro-content blocks may include the skills-based micro-contentblock 122 that is generated in view of the skill-sets of the user. Manyadditional types of micro-content blocks 116 may be generated withoutlimitations.

In an example, the various micro-content blocks 116 may includeinformation elements extracted from the processed information blocks bymapping onto each micro-task for relevancy, role of the user interactingfor executing a specific micro-task for relevancy, level of experienceor skill sets of the user for a particular micro-task that is containedwithin the workflow task 114 such that the one or more contextualmicro-content blocks 116 match to the plurality of micro-tasks of theworkflow task. Therefore, the various micro-content blocks 116 providesa set of structured digital information blocks that are relevant andrelated and deeply maps onto various micro-tasks when looked inassociation with the skillets of the user, location of the user, natureof micro-tasks and the overall workflow task 114, role of the user invarious micro-tasks, and the like. This may permit providing relevantinformation to the user when a particular task such as the workflow task114 is performed such that even if the user is not very familiar withhow to perform the workflow task 114, the user device 306 may bepermitted to receive necessary inputs from the processor 202 in terms ofthe micro-content blocks 116 that guides the user about the taskexecution not only at a broad level but at specific micro steps ormicro-tasks levels.

Each micro step or micro-task may be of a few seconds or minutesduration or may be longer. The duration of a micro-task may depend onthe level of guidance it needs as a standalone step independent of othersteps or micro-tasks. The workflow task 114 may be broken into themicro-tasks based on the level of guidance a portion of the entireworkflow task 114 may require for the user to effectively execute it.For example, a task may involve a series of five steps such that thefirst step involves understanding a particular function in order tostart a medical device, while the remaining four steps are merely tomonitor the different values generated by the device. In an example, thetask may be broken into two micro-tasks: a first micro-task involvingthe first step, and a second micro-task involving the remaining foursteps. In an embodiment, each step may be defined by a separatemicro-task or more than one micro-task. The procedure of determining thevarious micro-tasks based on the workflow task 114 is performed by atask manager 228.

The task manager 228 may be configured to manage various tasks such asdelivery of the micro content blocks in accordance with schedule of theworkflow task 114. The task manager 228 is communicatively coupled tothe processing circuit 202. The task manager 228 may schedule thedelivery of the micro-content blocks 116 according to schedule of thetasks and the associated micro-tasks and accordingly tie different taskson a time series and identifies timelines associated with the deliveryof the micro-content blocks 116 for the different tasks.

The task manager 228 examines the status of the micro-tasks on the timeseries and generates an automated output indicative of the task's statusand respective micro-content blocks delivery. The task manager 228 mayautomatically notify to the user about status of the micro-contentblocks delivery for the tasks as soon as they are delivered and/or theirtasks are complete or if the delivery is pending after due time. Thetask manager 228 may, in general, organize scheduling of the tasks asthey are to occur and accordingly connect with other systems andcomponents for allowing the delivery of the micro-content blocks 116without any delay and/or conflict.

In an embodiment, the task manager 228 may serve as a command center andcan track activities of the users such as who is reading, who islearning, and who is spending what time on what content as indicatedthrough tracking of the time spent by the users on the differentmicro-content blocks 116 delivered to them. Accordingly, intelligentrecommendations may be sent to the users based on their education habitsand those items that are more useful to their activities and tasks.

The micro-content blocks 116 that match and are related precisely tospecific micro-tasks associated with the workflow task 114 arecommunicated by the processor 202 to the user device 306. Themicro-content communication component 230 of the processor 202 transmitsthe one or more contextual micro-content blocks relating to the specificmicro-tasks of the workflow task 114 to the user device 306 associatedwith the user at a time when the workflow task 114 or a micro-task isabout to occur. The one or more contextual micro-content blocks 116 maybe time-stamped before transmission to the user device 306 associatedwith the user for real-time delivery according to the occurrence of theplurality of micro-tasks.

In various examples, the information sources 106 may be hosted byapplications such as websites or electronic applications or mobileapplications or computing machines associated with third-party companiesor third-party vendors (or merely third parties for simplicity ofdescription). The information blocks obtained from the informationsources 106 may be transformed into the micro-content blocks 116 basedon context data analysis, application data analysis, user data analysis,AI/ML by the information management server 104 so as to generatenecessary information presentable on a user screen in the form of themicro-content blocks 116 by the information management server 104.

The recorded or monitored context sensitive, application sensitive, oruser sensitive information (together referred to as contextual data orinformation or context sensitive data or information or contextuallysensitive data or information) by the front-end context monitoringappliance 310 may be supplied to the information management server 104for further processing of the information and generating and deliveringthe micro-content blocks 116 to the user device 306 as discussed above.The contextual data monitored by the front-end context monitoringappliance 310 may change with time and may also be different fordifferent tasks.

In some embodiments, the computerized data collection wireless appliance208 may be configured to perform certain data collection tasks withinthe network 110 digitally. In an example, the computerized datacollection wireless appliance 208 may be permitted to generate and/orcollect data from various devices such as medical devices, web pages,local data repositories, etc. together referred to as the informationsources 106 that operate within the network 110. The computerized datacollection wireless appliance 208 may be configured to collect, store,and digitally manage data at the information management server levelthat is extracted or collected from the information sources 106.

The network 110 may broadly represent one or more LANs, WANs, cellularnetworks (e.g., LTE, HSPA, 3G, and other cellular technologies, etc.),global internet, and/or networks using any of wired, wireless,terrestrial microwave, or satellite links, and may include the publicInternet.

In FIG. 2 , a network layer 236 may be provided that may use a pushgateway system 238 and/or a pull gateway system 240 to collect the datafrom the information sources 106. In the pull gateway system 240, thenetwork layer 236 may be permitted to send or ask queries to theinformation sources 106. In response to the queries, acomputer-executable file containing information may be pulled in by thenetwork layer 236 from the information sources 106. In the push gatewaysystem 238, the network layer 236 may monitor various data elements atthe information sources 106 and the information sources 106 may pushrelevant data in the form of the computer-executable files into thenetwork layer 236 to get transferred to a central database or a localdata reservoir 226.

The data collected from the information sources 106 may be stored in thelocal data reservoir 226 either for a small period of time just beforeit gets processed by the processor 202 or for a longer time such as aretention period as necessary.

The devices or machines that may generate the digital data extracted bythe computerized data collection wireless appliance 208 or itscomponents (exporters) thereof may include such as medical devices,FHIR-capable systems (EHRs), HL7-capable systems (EHRs or electronichealth record systems), Non-HL7/FHIR EHRs, etc., source databases,safety/error reporting systems, global web pages, social media posts,local data repositories and the like.

FIG. 4 , with reference to FIGS. 1 through 3 , illustrates an exemplaryblockchain-configured ecosystem architecture 400 containing one or morecomponents of the system 102 and also contain additional components soas to allow integrity of transactions and the digital data (includingthe information blocks and the micro-content blocks) shared/processedduring the transfer or storage as discussed above in the document. Theblockchain-configured ecosystem architecture 400 may provide acrowdsourced integrity network for storing the data accessed orextracted or transformed for sharing or storing across the network 110instead of locally stored information by different participants ordatabases that may be tampered with.

The ecosystem architecture 400 may be blockchain-configured involvingvarious blockchain devices. For example, the information managementserver 104 may interact with a blockchain device 402 through a pluralityof blockchain configured distributed access points 404. A network thatfacilitates interaction across all components may be a blockchainintegrity network. The blockchain network may build trust among thevarious participants or entities or systems or components thereof andtheir associated computing terminals or devices even if thedevices/terminals or machines etc. may not “know” one another. Theblockchain network may allow connections and transactions and recordingand sharing of the data, information blocks, micro-content blocks, andvarious codes/token generated during an entire transaction includingservice tokens and authorization tokens in a trusted mode. A record oftransactions and sharing and data from various terminals/devices storedon the blockchain in the form of computer-executable distributed ledgers406 may provide proof to command the necessary trust among theterminals/devices (such as those associated with variousparticipants/nodes etc. without limitations) to cooperate through apeer-to-peer or peer-to-client distributed digital ledger technologysystem. The ecosystem architecture 400 may include a distributed trustedledgers system 414 containing the distributed blockchain ledgers 406associated with a plurality of computing terminals and devices such thateach ledger stores a copy of computer-executable files 416 containingthe context inputs, context patterns, micro-content blocks, informationblocks, information extracted from the information sources 106, andvarious other details corresponding to the tasks such as the workflowtask 114 and the trust notes for defining security and trust among thecomputing terminals and devices across the network so that eachcomputing terminal trusts the other computing terminal through theblockchain. The distributed ledgers system 414 enables coding ofrules-based contracts that execute when specified conditions are met.The distributed ledgers 406 make it easier to create cost-efficientnetworks where any device or any evidence associated with a taskexecution or transaction may be tracked, without requiring a centralpoint of control.

The various computing terminals or devices in the network serve asdistributed peer-to-peer nodes and connections. The informationmanagement server 104 and its components thereof may be configured toperform the task of processing the context inputs and the informationblocks further through the blockchain network based on the rules asdefined and discussed herein. Each terminal/device/node in the ecosystemarchitecture 400, etc. may receive a copy of the blockchain which mayget downloaded automatically upon joining the blockchain integritynetwork. Every permissioned node or the device in the network is anadministrator of the blockchain, and may join the network voluntarily sothat the network is decentralized.

The blockchain may eliminate the risks that come with data being heldcentrally by storing data across the network which may include thecomputer-executable files 416 containing the information blocks, contextinputs, context patterns, etc. and/or the various tokens/codes includingtransaction codes. The blockchain security use encryption technology andvalidation mechanisms for security and integrity verification. Thesecurity may be enabled through public and private keys. A public keymay define a user's address on the blockchain. The private key may giveits owner an access to various digital assets in the network.

In an embodiment, the distributed ledgers 406 may enable coding of smartcontracts (with the use of such as smart contract systems) that willexecute when specified conditions are met. These smart contracts mayprotect various information pieces associated with the servicedeliveries and other transactions and data processing/storage andeliminate the risk of files copying and redistribution withoutprotecting privacy rights.

The blockchain-configured ecosystem architecture 400 may provide aprivate view for the various devices and the entities operating in thenetwork through the private data store 418 so that each permissioneddevice such as the information management server 104 may privatelyaccess the computer-executable files 416 associated with a task based onvarious policies such as based on their respective identities. Theinformation management server 104 may access the computer-executablefiles 416 through the dedicated private data store 418 available throughthe plurality of distributed blockchain-configured access points 404,which may be enabled in the form of distributed blocks as shown in FIG.4 , with each block providing the ability to access the features of theblockchain-configured ecosystem architecture 400 by different terminalsand devices at the same time based on defined and granted access rights.

The private data store 418 may provide a virtual storage to facilitateinteraction, information exchange, reviewing, and presentation of thecomputer-executable files 416. For example, the private data store 418may allow a virtual storage and presentation of only limited executablefiles or portions of the executable files for access by particularentities or participants in accordance with permissions granted forreviewing. The private data store 418 may be configured to auto-hashreview interactions at any required interval. This compartmentalizationof the computer-executable files 416 ensures that thecomputer-executable files 416 are secured and private as per accessrights authorized to the nodes. The data presented on the private datastore 418 of the blockchain serves as a secure way to ensure that theprivate data store 418 is in sync with any permissioned access.

In an embodiment, the blockchain-configured digital ecosystemarchitecture 400 may provide a federated blockchain comprising ofseveral entities/participants (including the user) and their associatedcomputers and devices (such as the user device 306) and sensors thatjointly interact to process transfers of data through a trusted, securedand distributed network of the blockchain-configured access points 404.

The various components described herein and/or illustrated in thefigures may be embodied as hardware-enabled modules and may be aplurality of overlapping or independent electronic circuits, devices,and discrete elements packaged onto a circuit board to provide data andsignal processing functionality within a computer. An example might be acomparator, inverter, or flip-flop, which could include a plurality oftransistors and other supporting devices and circuit elements. Themodules that include electronic circuits process computer logicinstructions capable of providing digital and/or analog signals forperforming various functions as described herein. The various functionscan further be embodied and physically saved as any of data structures,data paths, data objects, data object models, object files, databasecomponents. For example, the data objects could include a digital packetof structured data. Example data structures may include any of an array,tuple, map, union, variant, set, graph, tree, node, and an object, whichmay be stored and retrieved by computer memory and may be managed byprocessors, compilers, and other computer hardware components. The datapaths can be part of a computer CPU that performs operations andcalculations as instructed by the computer logic instructions. The datapaths could include digital electronic circuits, multipliers, registers,and buses capable of performing data processing operations andarithmetic operations (e.g., Add, Subtract, etc.), bitwise logicaloperations (AND, OR, XOR, etc.), bit shift operations (e.g., arithmetic,logical, rotate, etc.), complex operations (e.g., using single clockcalculations, sequential calculations, iterative calculations, etc.).The data objects may be physical locations in computer memory and can bea variable, a data structure, or a function. Some examples of themodules include relational databases (e.g., such as Oracle® relationaldatabases), and the data objects can be a table or column, for example.Other examples include specialized objects, distributed objects,object-oriented programming objects, and semantic web objects. The dataobject models can be an application programming interface for creatingHyperText Markup Language (HTML) and Extensible Markup Language (XML)electronic documents. The models can be any of a tree, graph, container,list, map, queue, set, stack, and variations thereof, according to someexamples. The data object files can be created by compilers andassemblers and contain generated binary code and data for a source file.The database components can include any of tables, indexes, views,stored procedures, and triggers.

In an example, the embodiments herein can provide a computer programproduct configured to include a pre-configured set of instructions,which when performed, can result in actions as stated in conjunctionwith various figures herein. In an example, the pre-configured set ofinstructions can be stored on a tangible non-transitory computerreadable medium. In an example, the tangible non-transitory computerreadable medium can be configured to include the set of instructions,which when performed by a device, can cause the device to perform actssimilar to the ones described here.

The embodiments herein may also include tangible and/or non-transitorycomputer-readable storage media for carrying or havingcomputer-executable instructions or data structures stored thereon. Suchnon-transitory computer readable storage media can be any availablemedia that can be accessed by a general purpose or special purposecomputer, including the functional design of any special purposeprocessor as discussed above. By way of example, and not limitation,such non-transitory computer-readable media can include RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium which can be used tocarry or store desired program code means in the form ofcomputer-executable instructions, data structures, or processor chipdesign. When information is transferred or provided over a network oranother communications connection (either hardwired, wireless, orcombination thereof) to a computer, the computer properly views theconnection as a computer-readable medium. Thus, any such connection isproperly termed a computer-readable medium. Combinations of the aboveshould also be included within the scope of the computer-readable media.

Computer-executable instructions include, for example, instructions anddata which cause a special purpose computer or special purposeprocessing device to perform a certain function or group of functions.Computer-executable instructions also include program modules that areexecuted by computers in stand-alone or network environments. Generally,program modules include routines, programs, components, data structures,objects, and the functions inherent in the design of special-purposeprocessors, etc. that perform particular tasks or implement particularabstract data types. Computer-executable instructions, associated datastructures, and program modules represent examples of the program codemeans for executing steps of the methods disclosed herein. Theparticular sequence of such executable instructions or associated datastructures represents examples of corresponding acts for implementingthe functions described in such steps.

The techniques provided by the embodiments herein may be implemented onan integrated circuit chip (not shown). The chip design is created in agraphical computer programming language, and stored in a computerstorage medium (such as a disk, tape, physical hard drive, or virtualhard drive such as in a storage access network. If the designer does notfabricate chips or the photolithographic masks used to fabricate chips,the designer transmits the resulting design by physical means (e.g., byproviding a copy of the storage medium storing the design) orelectronically (e.g., through the Internet) to such entities, directlyor indirectly. The stored design is then converted into the appropriateformat (e.g., GDSII) for the fabrication of photolithographic masks,which typically include multiple copies of the chip design in questionthat are to be formed on a wafer. The photolithographic masks areutilized to define areas of the wafer (and/or the layers thereon) to beetched or otherwise processed.

The resulting integrated circuit chips can be distributed by thefabricator in raw wafer form (that is, as a single wafer that hasmultiple unpackaged chips), as a bare die, or in a packaged form. In thelatter case the chip is mounted in a single chip package (such as aplastic carrier, with leads that are affixed to a motherboard or otherhigher level carrier) or in a multichip package (such as a ceramiccarrier that has either or both surface interconnections or buriedinterconnections). In any case the chip is then integrated with otherchips, discrete circuit elements, and/or other signal processing devicesas part of either (a) an intermediate product, such as a motherboard, or(b) an end product. The end product can be any product that includesintegrated circuit chips, ranging from toys and other low-endapplications to advanced computer products having a display, a keyboardor other input device, and a central processor.

Furthermore, the embodiments herein can take the form of a computerprogram product accessible from a computer-usable or computer-readablemedium providing program code for use by or in connection with acomputer or any instruction execution system. For the purposes of thisdescription, a computer-usable or computer readable medium can be anyapparatus that can comprise, store, communicate, propagate, or transportthe program for use by or in connection with the instruction executionsystem, apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include asemiconductor or solid-state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk and an optical disk. Current examples of opticaldisks include compact disk-read only memory (CD-ROM), compactdisk-read/write (CD-R/W) and DVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output (I/O) devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modem and Ethernet cards are just a few of the currently availabletypes of network adapters.

A representative hardware environment for practicing the embodimentsherein is depicted in FIG. 5 , with reference to FIGS. 1 through 4 .This schematic drawing illustrates a hardware configuration of aninformation handling/computer system 500 in accordance with theembodiments herein. The system 500 comprises at least one processor orcentral processing unit (CPU) 10. The CPUs 10 are interconnected viasystem bus 12 to various devices such as a random access memory (RAM)14, read-only memory (ROM) 16, and an input/output (I/O) adapter 18. TheI/O adapter 18 can connect to peripheral devices, such as disk units 11and tape drives 13, or other program storage devices that are readableby the system. The system 500 can read the inventive instructions on theprogram storage devices and follow these instructions to execute themethodology of the embodiments herein. The system 500 further includes auser interface adapter 19 that connects a keyboard 15, mouse 17, speaker24, microphone 22, and/or other user interface devices such as a touchscreen device (not shown) to the bus 12 to gather user input.Additionally, a communication adapter 20 connects the bus 12 to a dataprocessing network, and a display adapter 21 connects the bus 12 to adisplay device 23 which may be embodied as an output device such as amonitor, printer, or transmitter, for example. Further, a transceiver26, a signal comparator 27, and a signal converter 28 may be connectedwith the bus 12 for processing, transmission, receipt, comparison, andconversion of electric or electronic signals.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the embodiments herein that others can, byapplying current knowledge, readily modify and/or adapt for variousapplications such specific embodiments without departing from thegeneric concept, and, therefore, such adaptations and modificationsshould and are intended to be comprehended within the meaning and rangeof equivalents of the disclosed embodiments. It is to be understood thatthe phraseology or terminology employed herein is for the purpose ofdescription and not of limitation. Therefore, while the embodimentsherein have been described in terms of preferred embodiments, thoseskilled in the art will recognize that the embodiments herein can bepracticed with modification within the spirit and scope of the appendedclaims.

What is claimed is:
 1. A method for live digital streaming of one ormore contextual micro-content blocks in real-time for a workflow task tofacilitate task performance by a user, the method comprising: receivingone or more digital inputs, wherein each digital input is indicative ofone or more of a parameters of relevance associated with the workflowtask; navigating through one or more digital information sourcesaccessible over a network to search for information that matches the oneor more parameters of relevance associated with the workflow task;generating one or more contextual micro-content blocks based on anoverall contextual pattern associated with the workflow task; andtransmitting the one or more contextual micro-content blocks relating tothe workflow task to a device associated with the user to deliverinformation pertinent to the overall contextual pattern of the workflowtask.
 2. The method of claim 1, comprising generating the overallcontextual pattern defined by a score that represents a context of theworkflow task.
 3. The method of claim 1, wherein the one or morecontextual micro-content blocks relating to the workflow task istransmitted to the device associated with the user at a time when aplurality of micro-tasks of the workflow task are about to start.
 4. Themethod of claim 1, wherein the one or more contextual micro-contentblocks are time-stamped before transmission to the device associatedwith the user for real-time delivery according to an occurrence of aplurality of micro-tasks associated with the workflow task.
 5. Themethod of claim 4, comprising receiving a signal indicative of theplurality of micro-tasks specific to the user for real-time delivery ofthe one or more contextual micro-content blocks to the user device. 6.The method of claim 4, wherein a duration of a first micro task of theplurality of micro tasks is determined based on a level of guidance thatthe first micro task needs as a standalone step independent of a secondmicro task, where the second micro task is one of the plurality of microtasks other than the first micro task.
 7. The method of claim 1, whereingenerating the one or more contextual micro-content blocks based on thecontextual pattern associated with the workflow task comprises:extracting one or more computer-executable information files from theone or more digital information sources based on the contextual patternassociated with the workflow task; digitally processing the one or moreextracted computer-executable information files into a plurality ofprocessed information blocks; and generating the one or more contextualmicro-content blocks from the plurality of processed information blocks.8. The method of claim 1, comprising notifying to the user when the oneor more contextual micro-content blocks is at least one of delivered,completed, and remains pending after a specified due time.
 9. The methodof claim 1, comprising generating AWL-based predictions of futureexpected behavior and learning requirements by the user using anartificial intelligence machine and a machine learning tool to timelyguide when predefined information is required by the user.
 10. A systemfor live digital streaming of one or more contextual micro-contentblocks in real-time for a workflow task to facilitate task performanceby a user, wherein the system comprises: an information managementserver connected to a plurality of user devices associated with aplurality of users, wherein each of the user devices performs theworkflow task at a particular time and location such that performance ofthe workflow task occurs at a location remote from a location of theinformation management server, and wherein the workflow task comprises aplurality of micro-tasks performed in a particular sequence atparticular locations and particular time slots, wherein the informationmanagement server comprises a processor to: navigate through one or moredigital information sources accessible over a network to search forinformation that matches one or more parameters of relevance associatedwith the workflow task; generate a unified score for determining acontextual pattern associated with the workflow task based on the one ormore parameters of relevance associated with the workflow task; generateone or more contextual micro-content blocks based on the contextualpattern associated with the workflow task; and transmit the one or morecontextual micro-content blocks relating to the workflow task to adevice associated with the user at a time when a plurality ofmicro-tasks of the workflow task are about to start to deliverinformation pertinent to the contextual pattern of the workflow task.11. The system of claim 10, wherein the one or more contextualmicro-content blocks is time-stamped before transmission to the deviceassociated with the user for real-time delivery according to anoccurrence of a plurality of micro-tasks.
 12. The system of claim 11,wherein the processor tracks activities of the user, determines theplurality of micro-tasks specific to the activities of the user forreal-time delivery, determines recommendations for the user related tothe activities of the user, and generates the one or more micro-contentblocks that pertains to accomplish the plurality of micro-tasks specificto the user activities for real-time delivery of the one or more microcontent blocks.
 13. The system of claim 11, wherein a duration of afirst micro task of the plurality of micro tasks is determined based ona level of guidance that the first micro task needs as a standalone stepindependent of a second micro task, where the second micro task is oneof the plurality of micro tasks other than the first micro task.
 14. Thesystem of claim 10, wherein the processor generates the one or morecontextual micro-content blocks based on the contextual patternassociated with the workflow task, the processor further configured to:extract computer-executable information files from the one or moredigital information sources based on the contextual pattern associatedwith the workflow task; digitally process the extractedcomputer-executable information files into a plurality of processedinformation blocks; and generate the one or more contextualmicro-content blocks from the plurality of processed information blocks.15. The system of claim 10, wherein the processor notifies to the userwhen the one or more contextual micro-content blocks is at least one ofdelivered, completed, and remains pending after a specified due time.16. The system of claim 10, wherein the processor is configured togenerate one or more AWL-based predictions of future expected behaviorand learning requirements by the user using one or more of an artificialintelligence machine and a machine learning tool to timely guide whencertain information is required by the user.
 17. A system for digitalstreaming of one or more contextual micro-content blocks in real-timefor a workflow task to facilitate task performance by a user, whereinthe system comprises: an information management server connected to aplurality of user devices associated with a plurality of users, whereinthe information management server comprises a processor to: extractcomputer-executable information files from the one or more digitalinformation sources that matches the one or more parameters of relevancefor the workflow task, create a plurality of information blocks based onthe extracted computer-executable information files, generate one ormore contextual micro-content blocks from the plurality of informationblocks; and transmit the one or more contextual micro-content blocksrelating to the workflow task to a device associated with the user. 18.The system of claim 17, wherein the one or more micro-content blockscomprises at least one of a context-based micro-content block, alocation-based micro-content block, a role-based micro-content block,and a skills-based micro-content block.
 19. The system of claim 18,wherein the one or more contextual micro-content blocks are time-stampedbefore transmission to the device associated with the user for real-timedelivery of the one or more micro content blocks according to anoccurrence of the plurality of micro-tasks associated with the workflowtask.
 20. The system of claim 17, wherein the processor notifies theuser when the one or more contextual micro-content blocks is at leastone of delivered, completed, and remains pending after a specified duetime.